Real Time Video-Based Surveillance Detection System Using Deep Learning for Security Applications

The increasing demand for intelligent security systems has led to the development of automated video surveillance technologies capable of detecting suspicious activities in real time. Traditional surveillance systems rely heavily on human operators to monitor multiple camera feeds, which often leads to delayed responses and reduced efficiency. To address this limitation, this study proposes a Real-Time Video-Based Surveillance Detection System using Deep Learning for Security Applications. The proposed system utilizes deep learning–based object detection and tracking techniques to automatically analyze video streams and identify suspicious behaviors. Advanced models such as YOLO for object detection and DeepSORT for multi-object tracking are employed to detect individuals and analyze movement patterns. The system also incorporates behavior analysis techniques such as loitering detection and crowd monitoring to identify potential security threats. Experimental evaluation demonstrates that the proposed framework improves surveillance accuracy and enables timely alert generation. The system enhances situational awareness, reduces dependency on manual monitoring, and provides an effective solution for modern intelligent security systems.